mean that I still keep my core MP3 library synced with every mobile device I use. A few years back, I had settled on iTunes’s smart playlists as the most pain-free way of consuming said collection.

My insistance with keeping personal music collection necessities regular maintanence. As usual, I was adding a few new songs to the set, robotically assigning star ratings so they would be automatically catalogued by playlist filters. A few of them will eventually show up under the “4 stars and above” playthrough.

A pattern became apparent and soon annoying, however: the “4 stars and above” contained a number of songs that, at one point in time, were favorites but have since grown slightly long in the tooth. Given the choice between re-rating the music or just skipping a song, I again picked the easiest option and double clicked the mic button.

As fascinating as my music library maintanence may be, this isn’t a unique problem my habits. Music is one of a few fields in general computing where we’ve had a decade or more of input; we have described our preferences, detailed our relationships, and strung together a cacophony of metadata and information. Over time, much of this information is outdated, superseded, and simply less timely and valuable. Time degradation of data – and more interestingly, metadata – is not regularly modeled and understood.

It doesn’t even take decades-long timescales to reveal the problem. When Google Plus first launched, its innovative Circles were considered its killer feature – until some realized that the cost of maintenance could easily outweigh their potential benefit in filtering conversations. Other major online hubs face the same issue as well: some have recognized the advantages of trimming their Twitter follow lists to a manageable count, and Facebook keeps adding filters and algorithms to surface relevance in an increasing noisy social graph. Insightfully, Google highlighted the importance of time in its search indexing, a decade after it started (and has lead) the business of search.

Though ultimately, timely degradation itself is a heuristic for how data truly loses and perhaps gains importance. Maybe it’d be too much to try to model and understand how we perceive information; sometimes it’s just easier to play the next song.